Towards Private Data-driven Control
Towards Private Data-driven Control
复制标题
走向私有数据驱动控制
DOI：
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复制
发表时间：
2020
期刊：
影响因子：
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通讯作者：
George Pappas
中科院分区：
文献类型：
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作者：
A. Alexandru;Anastasios Tsiamis;George Pappas
控制即服务 (CaaS) 正在成为现实，尤其是在楼宇自动化和智能电网管理方面。通常，CaaS 中的控制算法侧重于直接从输入输出数据控制客户端的系统，因为系统的模型可能是私有的或不可用的。因此，需要将客户端采集的大量数据上传至云服务器。恶意云服务提供商可以使用这些数据来推断有关客户端的敏感信息并发起攻击。在本文中，我们共同设计了一个将控制和隐私交织在一起的解决方案。我们的目标是对加密的输入输出数据执行在线数据驱动控制，同时维护客户上传数据、所需设定值和控制操作的隐私。我们根据行为框架的结果设计控制算法，与其他经典框架相比，它对加密更加友好。我们通过使用分级同态加密方案来获得隐私，使云能够对客户端的加密数据进行复杂的计算。最后，我们通过操纵控制算法所需的任务，使其仅涉及算术电路，以及利用并行化和密文打包来提高效率。
Control as a Service (CaaS) is becoming a reality– particularly in the case of building automation and smart grid management. Often, the control algorithms in CaaS focus on controlling the client’s system directly from input-output data, since the system’s model might be private or unavailable. Therefore, large quantities of data collected from the client need to be uploaded to a cloud server. This data can be used by a malevolent cloud service provider to infer sensitive information about the client and mount attacks. In this paper, we co-design a solution that interlaces control and privacy. Our goal is to perform online data-driven control on encrypted input-output data, while maintaining the privacy of the client’s uploaded data, desired setpoint and control actions. We design our control algorithm based on results from the behavioral framework, which is more encryption-friendly compared to other classical frameworks. We obtain privacy by using a leveled homomorphic encryption scheme to enable the cloud to perform complex computations on the client’s encrypted data. Finally, we achieve efficiency by manipulating the tasks required by the control algorithm such that they only involve arithmetic circuits, as well as by leveraging parallelization and ciphertext packing.